Camouflaged Object Detection (COD) aims to visually seg- ment camouflaged objects that blend into their surrounding environment. The problems of inaccurate object localization and lack of detailed information necessary for precise segmentation have not been effectively resolved in current camouflaged object detection models, and these models generally have a large number of parameters. To address these issues, we propose a localization guidance and multi-scale refinement network (LRNet). Through effective aggregation and use of contextual and gradient information, the LRNet can achieve accurate localization and refined segmentation of camouflaged objects. Specifically, we develop a regional localization decoder (RLD) to locate the camouflaged object region by fusing multi-stage contextual information. Additionally, we employ an attention complementary fusion (ACF) module to enhance internal feature representation, which leverages gradient features to compensate for the limitations of contextual features in geometric texture expression. To accurately distinguish the camouflaged object from the background, we propose an iterative refinement fusion (IRF) module. The IRF module integrates adjacent-level features under the guidance of localization information to refine details at multiple scales. Extensive experimental results demonstrate the efficacy of our LRNet, which employs only 22.33M parameters and significantly outperforms SOTA methods on three widely used benchmark datasets.